{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "565dee1d",
   "metadata": {},
   "source": [
    "# DAY 24 元组和OS模块"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40753ff9",
   "metadata": {},
   "source": [
    "## 元组"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78026aae",
   "metadata": {},
   "source": [
    "元组的特点：\n",
    "1. 有序，可以重复，这一点和列表一样\n",
    "2. 元组中的元素不能修改，这一点非常重要，深度学习场景中很多参数、形状定义好了确保后续不能被修改。\n",
    "\n",
    "\n",
    "很多流行的 ML/DL 库（如 TensorFlow, PyTorch, NumPy）在其 API 中都广泛使用了元组来表示形状、配置等。\n",
    "\n",
    "可以看到，元组最重要的功能是在列表之上，增加了不可修改这个需求\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0d3139",
   "metadata": {},
   "source": [
    "### 元组的创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88cc2032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3)\n",
      "('a', 'b', 'c')\n",
      "(1, 'hello', 3.14, [4, 5])\n"
     ]
    }
   ],
   "source": [
    "my_tuple1 = (1, 2, 3)\n",
    "my_tuple2 = ('a', 'b', 'c')\n",
    "my_tuple3 = (1, 'hello', 3.14, [4, 5]) # 可以包含不同类型的元素\n",
    "print(my_tuple1)\n",
    "print(my_tuple2)\n",
    "print(my_tuple3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "967b3443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 20, 'thirty')\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "# 可以省略括号\n",
    "my_tuple4 = 10, 20, 'thirty' # 逗号是关键\n",
    "print(my_tuple4)\n",
    "print(type(my_tuple4)) # 看看它的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "951acb6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "()\n",
      "()\n"
     ]
    }
   ],
   "source": [
    "# 创建空元组\n",
    "empty_tuple = ()\n",
    "# 或者使用 tuple() 函数\n",
    "empty_tuple2 = tuple()\n",
    "print(empty_tuple)\n",
    "print(empty_tuple2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd78c51c",
   "metadata": {},
   "source": [
    "### 元组的常见用法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "446b02da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P\n",
      "t\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "# 元组的索引\n",
    "my_tuple = ('P', 'y', 't', 'h', 'o', 'n')\n",
    "print(my_tuple[0])  # 第一个元素\n",
    "print(my_tuple[2])  # 第三个元素\n",
    "print(my_tuple[-1]) # 最后一个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02a95803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3)\n",
      "(0, 1, 2)\n",
      "(3, 4, 5)\n",
      "(0, 2, 4)\n"
     ]
    }
   ],
   "source": [
    "# 元组的切片\n",
    "my_tuple = (0, 1, 2, 3, 4, 5)\n",
    "print(my_tuple[1:4])  # 从索引 1 到 3 (不包括 4)\n",
    "print(my_tuple[:3])   # 从开头到索引 2\n",
    "print(my_tuple[3:])   # 从索引 3 到结尾\n",
    "print(my_tuple[::2])  # 每隔一个元素取一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57272395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# 元组的长度获取\n",
    "my_tuple = (1, 2, 3)\n",
    "print(len(my_tuple))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d1a34c",
   "metadata": {},
   "source": [
    "管道工程中pipeline类接收的是一个包含多个小元组的 列表 作为输入。\n",
    "\n",
    "可以这样理解这个结构：\n",
    "\n",
    "1. 列表 []: 定义了步骤执行的先后顺序。Pipeline 会按照列表中的顺序依次处理数据。之所以用列表，是未来可以对这个列表进行修改。\n",
    "2. 元组 (): 用于将每个步骤的名称和处理对象捆绑在一起。名称用于在后续访问或设置参数时引用该步骤，而对象则是实际执行数据转换或模型训练的工具。固定了操作名+操作\n",
    "\n",
    "不用字典因为字典是无序的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f8eea7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型在测试集上的准确率: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 1. 加载数据\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 2. 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 3. 构建管道\n",
    "# 管道按顺序执行以下步骤：\n",
    "#    - StandardScaler(): 标准化数据（移除均值并缩放到单位方差）\n",
    "#    - LogisticRegression(): 逻辑回归分类器\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('logreg', LogisticRegression())\n",
    "])\n",
    "\n",
    "# 4. 训练模型\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# 5. 预测\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# 6. 评估模型\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"模型在测试集上的准确率: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadf9161",
   "metadata": {},
   "source": [
    "# 可迭代对象"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916fdf48",
   "metadata": {},
   "source": [
    "可迭代对象 (Iterable) 是 Python 中一个非常核心的概念。简单来说，一个可迭代对象就是指那些能够一次返回其成员（元素）的对象，让你可以在一个循环（比如 for 循环）中遍历它们。\n",
    "\n",
    "\n",
    "\n",
    "Python 中有很多内置的可迭代对象，目前我们见过的类型包括：\n",
    "\n",
    "* **序列类型 (Sequence Types):**\n",
    "    * `list` (列表)\n",
    "    * `tuple` (元组)\n",
    "    * `str` (字符串)\n",
    "    * `range` (范围)\n",
    "    \n",
    "* **集合类型 (Set Types):**\n",
    "    * `set` (集合)\n",
    " \n",
    "* **字典类型 (Mapping Types):**\n",
    "    * `dict` (字典) - 迭代时返回键 (keys)\n",
    "* **文件对象 (File objects)**\n",
    "* **生成器 (Generators)**\n",
    "* **迭代器 (Iterators) 本身**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "496bfa44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代列表:\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# 列表 (list)\n",
    "print(\"迭代列表:\")\n",
    "my_list = [1, 2, 3, 4, 5]\n",
    "for item in my_list:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0f800d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代元组:\n",
      "a\n",
      "b\n",
      "c\n"
     ]
    }
   ],
   "source": [
    "# 元组 (tuple)\n",
    "print(\"迭代元组:\")\n",
    "my_tuple = ('a', 'b', 'c')\n",
    "for item in my_tuple:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0792e85b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代字符串:\n",
      "h\n",
      "e\n",
      "l\n",
      "l\n",
      "o\n"
     ]
    }
   ],
   "source": [
    "# 字符串 (str)\n",
    "print(\"迭代字符串:\")\n",
    "my_string = \"hello\"\n",
    "for char in my_string:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b95428ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代 range:\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# range (范围)\n",
    "print(\"迭代 range:\")\n",
    "for number in range(5):  # 生成 0, 1, 2, 3, 4\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b41b64c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代集合:\n",
      "1\n",
      "3\n",
      "4\n",
      "5\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# 集合类型 (Set Types)\n",
    "\n",
    "# 集合 (set) - 注意集合是无序的，所以每次迭代的顺序可能不同\n",
    "print(\"迭代集合:\")\n",
    "my_set = {3, 1, 4, 1, 5, 9}\n",
    "for item in my_set:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fe4ef69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代字典 (默认迭代键):\n",
      "name\n",
      "age\n",
      "city\n"
     ]
    }
   ],
   "source": [
    "# 字典 (dict) - 默认迭代时返回键 (keys)\n",
    "print(\"迭代字典 (默认迭代键):\")\n",
    "my_dict = {'name': 'Alice', 'age': 30, 'city': 'Singapore'}\n",
    "for key in my_dict:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8030871",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代字典的值:\n",
      "Alice\n",
      "30\n",
      "Singapore\n"
     ]
    }
   ],
   "source": [
    "# 迭代字典的值 (values)\n",
    "print(\"迭代字典的值:\")\n",
    "for value in my_dict.values():\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a29a5447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代字典的键值对:\n",
      "Key: name, Value: Alice\n",
      "Key: age, Value: 30\n",
      "Key: city, Value: Singapore\n"
     ]
    }
   ],
   "source": [
    "# 迭代字典的键值对 (items)\n",
    "print(\"迭代字典的键值对:\")\n",
    "for key, value in my_dict.items(): # items方法很好用\n",
    "    print(f\"Key: {key}, Value: {value}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e555d63",
   "metadata": {},
   "source": [
    "## OS 模块"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49760e74",
   "metadata": {},
   "source": [
    "随着深度学习项目变得越来越大、数据量越来越多、代码结构越来越复杂，你会越来越频繁地用到 os 模块来管理文件、目录、路径，以及进行一些基本的操作系统交互。虽然深度学习的核心在于模型构建和训练，但数据和模型的有效管理是项目成功的关键环节，而 os 模块为此提供了重要的工具。\n",
    "\n",
    "在简单的入门级项目中，你可能只需要使用 pd.read_csv() 加载数据，而不需要直接操作文件路径。但是，当你开始处理图像数据集、自定义数据加载流程、保存和加载复杂的模型结构时，os 模块就会变得非常有用。\n",
    "\n",
    "好的代码组织和有效的文件管理是大型深度学习项目的基石。os 模块是实现这些目标的重要组成部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0535fe6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# os是系统内置模块，无需安装"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4276eb",
   "metadata": {},
   "source": [
    "### 获取当前工作目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "245a5cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\PC\\\\Desktop\\\\python训练营'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "os.getcwd() # get current working directory 获取当前工作目录的绝对路径"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac1d5da",
   "metadata": {},
   "source": [
    "###  获取当前工作目录下的文件列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0b5f16a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['day24 元组和OS模块.ipynb', '演示1']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir() # list directory 获取当前工作目录下的文件列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "161e8712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\YourUsername\\\\Documents\\\\MyProjectData\\\\results.csv'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#    我们使用 r'' 原始字符串，这样就不需要写双反斜杠 \\\\，因为\\会涉及到转义问题\n",
    "path_a = r'C:\\Users\\YourUsername\\Documents' # r''这个写法是写给python解释器看，他只会读取引号内的内容，不用在意r的存在会不会影响拼接\n",
    "path_b = 'MyProjectData'\n",
    "file = 'results.csv'\n",
    "\n",
    "# 使用 os.path.join 将它们安全地拼接起来，os.path.join 会自动使用 Windows 的反斜杠 '\\' 作为分隔符\n",
    "file_path = os.path.join(path_a , path_b, file)\n",
    "\n",
    "file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11696577",
   "metadata": {},
   "source": [
    "### 环境变量方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "135f28ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "environ{'ALLUSERSPROFILE': 'C:\\\\ProgramData',\n",
       "        'AMOSAPP': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26',\n",
       "        'AMOSDOCS': 'C:\\\\Users\\\\PC\\\\Documents\\\\AmosDevelopment\\\\Amos\\\\26',\n",
       "        'AMOSEXAMPLES': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26\\\\Examples\\\\English',\n",
       "        'AMOSLOGS': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26\\\\Logs',\n",
       "        'AMOSPLUGINS': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26\\\\Plugins',\n",
       "        'AMOSPROGRAM': 'D:\\\\Jupyter\\\\SEM\\\\Amos26',\n",
       "        'AMOSTEMPLATES': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26\\\\Templates\\\\English',\n",
       "        'AMOSTUTORIAL': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\AmosDevelopment\\\\Amos\\\\26\\\\Tutorial\\\\English',\n",
       "        'APPDATA': 'C:\\\\Users\\\\PC\\\\AppData\\\\Roaming',\n",
       "        'CHROME_CRASHPAD_PIPE_NAME': '\\\\\\\\.\\\\pipe\\\\crashpad_2824_TQDHDSUOXXPVVCJP',\n",
       "        'COMMONPROGRAMFILES': 'C:\\\\Program Files\\\\Common Files',\n",
       "        'COMMONPROGRAMFILES(X86)': 'C:\\\\Program Files (x86)\\\\Common Files',\n",
       "        'COMMONPROGRAMW6432': 'C:\\\\Program Files\\\\Common Files',\n",
       "        'COMPUTERNAME': 'DESKTOP-N2RLOJJ',\n",
       "        'COMSPEC': 'C:\\\\WINDOWS\\\\system32\\\\cmd.exe',\n",
       "        'CONDA_DEFAULT_ENV': 'vs',\n",
       "        'CONDA_EXE': 'D:\\\\Anaconda\\\\Scripts\\\\conda.exe',\n",
       "        'CONDA_PREFIX': 'D:\\\\Anaconda\\\\envs\\\\vs',\n",
       "        'CONDA_PROMPT_MODIFIER': '(vs) ',\n",
       "        'CONDA_PYTHON_EXE': 'D:\\\\Anaconda\\\\python.exe',\n",
       "        'CONDA_ROOT': 'D:\\\\Anaconda',\n",
       "        'CONDA_SHLVL': '1',\n",
       "        'CUDA_PATH': 'C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v11.3',\n",
       "        'CUDA_PATH_V11_3': 'C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v11.3',\n",
       "        'CUDA_PATH_V12_1': 'C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v12.1',\n",
       "        'CUDA_VISIBLE_DEVICES': '0',\n",
       "        'DRIVERDATA': 'C:\\\\Windows\\\\System32\\\\Drivers\\\\DriverData',\n",
       "        'ELECTRON_RUN_AS_NODE': '1',\n",
       "        'FPS_BROWSER_APP_PROFILE_STRING': 'Internet Explorer',\n",
       "        'FPS_BROWSER_USER_PROFILE_STRING': 'Default',\n",
       "        'HF_HOME': 'E:\\\\cache\\\\huggingface_cache',\n",
       "        'HOMEDRIVE': 'C:',\n",
       "        'HOMEPATH': '\\\\Users\\\\PC',\n",
       "        'JPY_INTERRUPT_EVENT': '4256',\n",
       "        'LOCALAPPDATA': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local',\n",
       "        'LOGONSERVER': '\\\\\\\\DESKTOP-N2RLOJJ',\n",
       "        'NUMBER_OF_PROCESSORS': '24',\n",
       "        'NVCUDASAMPLES11_3_ROOT': 'C:\\\\ProgramData\\\\NVIDIA Corporation\\\\CUDA Samples\\\\v11.3',\n",
       "        'NVCUDASAMPLES_ROOT': 'C:\\\\ProgramData\\\\NVIDIA Corporation\\\\CUDA Samples\\\\v11.3',\n",
       "        'NVTOOLSEXT_PATH': 'C:\\\\Program Files\\\\NVIDIA Corporation\\\\NvToolsExt\\\\',\n",
       "        'OLLAMA_MODELS': 'E:\\\\ollamamodels',\n",
       "        'ONEDRIVE': 'C:\\\\Users\\\\PC\\\\OneDrive',\n",
       "        'ORIGINAL_XDG_CURRENT_DESKTOP': 'undefined',\n",
       "        'OS': 'Windows_NT',\n",
       "        'PATH': 'd:\\\\Anaconda\\\\envs\\\\vs;D:\\\\Anaconda\\\\envs\\\\vs;D:\\\\Anaconda\\\\envs\\\\vs\\\\Library\\\\mingw-w64\\\\bin;D:\\\\Anaconda\\\\envs\\\\vs\\\\Library\\\\usr\\\\bin;D:\\\\Anaconda\\\\envs\\\\vs\\\\Library\\\\bin;D:\\\\Anaconda\\\\envs\\\\vs\\\\Scripts;D:\\\\Anaconda\\\\envs\\\\vs\\\\bin;D:\\\\Anaconda\\\\condabin;C:\\\\Program Files (x86)\\\\Common Files\\\\Oracle\\\\Java\\\\javapath;c:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\Programs\\\\cursor\\\\resources\\\\app\\\\bin;C:\\\\Windows\\\\system32;C:\\\\Windows;C:\\\\Windows\\\\System32\\\\Wbem;C:\\\\Windows\\\\System32\\\\WindowsPowerShell\\\\v1.0;C:\\\\Windows\\\\System32\\\\OpenSSH;C:\\\\Program Files (x86)\\\\NVIDIA Corporation\\\\PhysX\\\\Common;C:\\\\Program Files\\\\Bandizip;C:\\\\WINDOWS\\\\system32;C:\\\\WINDOWS;C:\\\\WINDOWS\\\\System32\\\\Wbem;C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0;C:\\\\WINDOWS\\\\System32\\\\OpenSSH;C:\\\\Program Files (x86)\\\\PDFtk Server\\\\bin;E:\\\\NEMA\\\\JRE\\\\bin\\\\client;D:\\\\Git\\\\cmd;C:\\\\Program Files (x86)\\\\Common Files\\\\Business Objects\\\\3.0\\\\bin;C:\\\\Program Files (x86)\\\\Common Files\\\\Business Objects\\\\3.0\\\\crystalreportviewers11\\\\ActiveXControls;D:\\\\Anaconda;D:\\\\Anaconda\\\\Scripts;D:\\\\Anaconda\\\\Library\\\\bin;C:\\\\Program Files\\\\dotnet;C:\\\\Program Files\\\\NVIDIA Corporation\\\\Nsight Compute 2021.1.0;C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA;C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v11.3\\\\lib\\\\x64;C:\\\\Program Files\\\\NVIDIA GPU Computing Toolkit\\\\CUDA\\\\v11.3\\\\bin;C:\\\\Program Files\\\\NVIDI;D:\\\\soft_uncode\\\\Tesseract-OCR;D:\\\\soft_uncode\\\\微信web开发者工具\\\\dll;C:\\\\WINDOWS\\\\system32;C:\\\\WINDOWS;C:\\\\WINDOWS\\\\System32\\\\Wbem;C:\\\\WINDOWS\\\\System32\\\\WindowsPowerShell\\\\v1.0;C:\\\\WINDOWS\\\\System32\\\\OpenSSH;C:\\\\Program Files\\\\Google\\\\Chrome\\\\Application;C:\\\\Program Files\\\\NVIDIA Corporation\\\\NVIDIA app\\\\NvDLISR;D:\\\\soft_code\\\\Graphviz\\\\bin;D:\\\\soft_uncode\\\\pcsuite;d:\\\\soft_code\\\\Trae CN\\\\bin;D:\\\\vscode\\\\Microsoft VS Code\\\\bin;D:\\\\neo4jaa\\\\neo4j-community-5.12.0\\\\bin;C:\\\\Program Files\\\\Java\\\\jdk-21\\\\bin;C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps',\n",
       "        'PATHEXT': '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC',\n",
       "        'PROCESSOR_ARCHITECTURE': 'AMD64',\n",
       "        'PROCESSOR_IDENTIFIER': 'Intel64 Family 6 Model 151 Stepping 2, GenuineIntel',\n",
       "        'PROCESSOR_LEVEL': '6',\n",
       "        'PROCESSOR_REVISION': '9702',\n",
       "        'PROGRAMDATA': 'C:\\\\ProgramData',\n",
       "        'PROGRAMFILES': 'C:\\\\Program Files',\n",
       "        'PROGRAMFILES(X86)': 'C:\\\\Program Files (x86)',\n",
       "        'PROGRAMW6432': 'C:\\\\Program Files',\n",
       "        'PROMPT': '(vs) $P$G',\n",
       "        'PSMODULEPATH': '%ProgramFiles%\\\\WindowsPowerShell\\\\Modules;C:\\\\WINDOWS\\\\system32\\\\WindowsPowerShell\\\\v1.0\\\\Modules',\n",
       "        'PUBLIC': 'C:\\\\Users\\\\Public',\n",
       "        'PYDEVD_IPYTHON_COMPATIBLE_DEBUGGING': '1',\n",
       "        'PYTHONIOENCODING': 'utf-8',\n",
       "        'PYTHONUNBUFFERED': '1',\n",
       "        'PYTHONUTF8': '1',\n",
       "        'PYTHON_FROZEN_MODULES': 'on',\n",
       "        'SSL_CERT_FILE': 'D:\\\\Anaconda\\\\envs\\\\vs\\\\Library\\\\ssl\\\\cacert.pem',\n",
       "        'SYSTEMDRIVE': 'C:',\n",
       "        'SYSTEMROOT': 'C:\\\\WINDOWS',\n",
       "        'TEMP': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\Temp',\n",
       "        'TESSDATA_PREFIX': 'D:\\\\soft_uncode\\\\Tesseract-OCR\\\\',\n",
       "        'TMP': 'C:\\\\Users\\\\PC\\\\AppData\\\\Local\\\\Temp',\n",
       "        'USERDOMAIN': 'DESKTOP-N2RLOJJ',\n",
       "        'USERDOMAIN_ROAMINGPROFILE': 'DESKTOP-N2RLOJJ',\n",
       "        'USERNAME': 'PC',\n",
       "        'USERPROFILE': 'C:\\\\Users\\\\PC',\n",
       "        'VSCODE_CODE_CACHE_PATH': 'C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\CachedData\\\\19e0f9e681ecb8e5c09d8784acaa601316ca4571',\n",
       "        'VSCODE_CRASH_REPORTER_PROCESS_TYPE': 'extensionHost',\n",
       "        'VSCODE_CWD': 'C:\\\\Users\\\\PC\\\\Desktop\\\\vscode工作区',\n",
       "        'VSCODE_DOTNET_INSTALL_TOOL_ORIGINAL_HOME': 'undefined',\n",
       "        'VSCODE_ESM_ENTRYPOINT': 'vs/workbench/api/node/extensionHostProcess',\n",
       "        'VSCODE_HANDLES_UNCAUGHT_ERRORS': 'true',\n",
       "        'VSCODE_IPC_HOOK': '\\\\\\\\.\\\\pipe\\\\ccdd4d73-1.100.0-main-sock',\n",
       "        'VSCODE_L10N_BUNDLE_LOCATION': 'file:///c%3A/Users/PC/.vscode/extensions/ms-ceintl.vscode-language-pack-zh-hans-1.100.2025050709/translations/extensions/vscode.json-language-features.i18n.json',\n",
       "        'VSCODE_NLS_CONFIG': '{\"userLocale\":\"zh-cn\",\"osLocale\":\"zh-cn\",\"resolvedLanguage\":\"zh-cn\",\"defaultMessagesFile\":\"D:\\\\\\\\vscode\\\\\\\\Microsoft VS Code\\\\\\\\resources\\\\\\\\app\\\\\\\\out\\\\\\\\nls.messages.json\",\"languagePack\":{\"translationsConfigFile\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\tcf.json\",\"messagesFile\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\19e0f9e681ecb8e5c09d8784acaa601316ca4571\\\\\\\\nls.messages.json\",\"corruptMarkerFile\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\corrupted.info\"},\"locale\":\"zh-cn\",\"availableLanguages\":{\"*\":\"zh-cn\"},\"_languagePackId\":\"4000923e07438a458172c6e7b57c9479.zh-cn\",\"_languagePackSupport\":true,\"_translationsConfigFile\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\tcf.json\",\"_cacheRoot\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\",\"_resolvedLanguagePackCoreLocation\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\19e0f9e681ecb8e5c09d8784acaa601316ca4571\",\"_corruptedFile\":\"C:\\\\\\\\Users\\\\\\\\PC\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\Code\\\\\\\\clp\\\\\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\\\\\corrupted.info\"}',\n",
       "        'VSCODE_PID': '2824',\n",
       "        'WINDIR': 'C:\\\\WINDOWS',\n",
       "        '_CONDA_OLD_CHCP': '936',\n",
       "        '__CONDA_OPENSLL_CERT_FILE_SET': '\"1\"',\n",
       "        'PYDEVD_USE_FRAME_EVAL': 'NO',\n",
       "        'TERM': 'xterm-color',\n",
       "        'CLICOLOR': '1',\n",
       "        'FORCE_COLOR': '1',\n",
       "        'CLICOLOR_FORCE': '1',\n",
       "        'PAGER': 'cat',\n",
       "        'GIT_PAGER': 'cat',\n",
       "        'MPLBACKEND': 'module://matplotlib_inline.backend_inline',\n",
       "        'KMP_DUPLICATE_LIB_OK': 'True',\n",
       "        'KMP_INIT_AT_FORK': 'FALSE'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os.environ 表现得像一个字典，包含所有的环境变量\n",
    "os.environ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b4587707",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALLUSERSPROFILE=C:\\ProgramData\n",
      "AMOSAPP=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\n",
      "AMOSDOCS=C:\\Users\\PC\\Documents\\AmosDevelopment\\Amos\\26\n",
      "AMOSEXAMPLES=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\\Examples\\English\n",
      "AMOSLOGS=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\\Logs\n",
      "AMOSPLUGINS=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\\Plugins\n",
      "AMOSPROGRAM=D:\\Jupyter\\SEM\\Amos26\n",
      "AMOSTEMPLATES=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\\Templates\\English\n",
      "AMOSTUTORIAL=C:\\Users\\PC\\AppData\\Local\\AmosDevelopment\\Amos\\26\\Tutorial\\English\n",
      "APPDATA=C:\\Users\\PC\\AppData\\Roaming\n",
      "CHROME_CRASHPAD_PIPE_NAME=\\\\.\\pipe\\crashpad_2824_TQDHDSUOXXPVVCJP\n",
      "COMMONPROGRAMFILES=C:\\Program Files\\Common Files\n",
      "COMMONPROGRAMFILES(X86)=C:\\Program Files (x86)\\Common Files\n",
      "COMMONPROGRAMW6432=C:\\Program Files\\Common Files\n",
      "COMPUTERNAME=DESKTOP-N2RLOJJ\n",
      "COMSPEC=C:\\WINDOWS\\system32\\cmd.exe\n",
      "CONDA_DEFAULT_ENV=vs\n",
      "CONDA_EXE=D:\\Anaconda\\Scripts\\conda.exe\n",
      "CONDA_PREFIX=D:\\Anaconda\\envs\\vs\n",
      "CONDA_PROMPT_MODIFIER=(vs) \n",
      "CONDA_PYTHON_EXE=D:\\Anaconda\\python.exe\n",
      "CONDA_ROOT=D:\\Anaconda\n",
      "CONDA_SHLVL=1\n",
      "CUDA_PATH=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\n",
      "CUDA_PATH_V11_3=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\n",
      "CUDA_PATH_V12_1=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.1\n",
      "CUDA_VISIBLE_DEVICES=0\n",
      "DRIVERDATA=C:\\Windows\\System32\\Drivers\\DriverData\n",
      "ELECTRON_RUN_AS_NODE=1\n",
      "FPS_BROWSER_APP_PROFILE_STRING=Internet Explorer\n",
      "FPS_BROWSER_USER_PROFILE_STRING=Default\n",
      "HF_HOME=E:\\cache\\huggingface_cache\n",
      "HOMEDRIVE=C:\n",
      "HOMEPATH=\\Users\\PC\n",
      "JPY_INTERRUPT_EVENT=4256\n",
      "LOCALAPPDATA=C:\\Users\\PC\\AppData\\Local\n",
      "LOGONSERVER=\\\\DESKTOP-N2RLOJJ\n",
      "NUMBER_OF_PROCESSORS=24\n",
      "NVCUDASAMPLES11_3_ROOT=C:\\ProgramData\\NVIDIA Corporation\\CUDA Samples\\v11.3\n",
      "NVCUDASAMPLES_ROOT=C:\\ProgramData\\NVIDIA Corporation\\CUDA Samples\\v11.3\n",
      "NVTOOLSEXT_PATH=C:\\Program Files\\NVIDIA Corporation\\NvToolsExt\\\n",
      "OLLAMA_MODELS=E:\\ollamamodels\n",
      "ONEDRIVE=C:\\Users\\PC\\OneDrive\n",
      "ORIGINAL_XDG_CURRENT_DESKTOP=undefined\n",
      "OS=Windows_NT\n",
      "PATH=d:\\Anaconda\\envs\\vs;D:\\Anaconda\\envs\\vs;D:\\Anaconda\\envs\\vs\\Library\\mingw-w64\\bin;D:\\Anaconda\\envs\\vs\\Library\\usr\\bin;D:\\Anaconda\\envs\\vs\\Library\\bin;D:\\Anaconda\\envs\\vs\\Scripts;D:\\Anaconda\\envs\\vs\\bin;D:\\Anaconda\\condabin;C:\\Program Files (x86)\\Common Files\\Oracle\\Java\\javapath;c:\\Users\\PC\\AppData\\Local\\Programs\\cursor\\resources\\app\\bin;C:\\Windows\\system32;C:\\Windows;C:\\Windows\\System32\\Wbem;C:\\Windows\\System32\\WindowsPowerShell\\v1.0;C:\\Windows\\System32\\OpenSSH;C:\\Program Files (x86)\\NVIDIA Corporation\\PhysX\\Common;C:\\Program Files\\Bandizip;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0;C:\\WINDOWS\\System32\\OpenSSH;C:\\Program Files (x86)\\PDFtk Server\\bin;E:\\NEMA\\JRE\\bin\\client;D:\\Git\\cmd;C:\\Program Files (x86)\\Common Files\\Business Objects\\3.0\\bin;C:\\Program Files (x86)\\Common Files\\Business Objects\\3.0\\crystalreportviewers11\\ActiveXControls;D:\\Anaconda;D:\\Anaconda\\Scripts;D:\\Anaconda\\Library\\bin;C:\\Program Files\\dotnet;C:\\Program Files\\NVIDIA Corporation\\Nsight Compute 2021.1.0;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\lib\\x64;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin;C:\\Program Files\\NVIDI;D:\\soft_uncode\\Tesseract-OCR;D:\\soft_uncode\\微信web开发者工具\\dll;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0;C:\\WINDOWS\\System32\\OpenSSH;C:\\Program Files\\Google\\Chrome\\Application;C:\\Program Files\\NVIDIA Corporation\\NVIDIA app\\NvDLISR;D:\\soft_code\\Graphviz\\bin;D:\\soft_uncode\\pcsuite;d:\\soft_code\\Trae CN\\bin;D:\\vscode\\Microsoft VS Code\\bin;D:\\neo4jaa\\neo4j-community-5.12.0\\bin;C:\\Program Files\\Java\\jdk-21\\bin;C:\\Users\\PC\\AppData\\Local\\Microsoft\\WindowsApps\n",
      "PATHEXT=.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC\n",
      "PROCESSOR_ARCHITECTURE=AMD64\n",
      "PROCESSOR_IDENTIFIER=Intel64 Family 6 Model 151 Stepping 2, GenuineIntel\n",
      "PROCESSOR_LEVEL=6\n",
      "PROCESSOR_REVISION=9702\n",
      "PROGRAMDATA=C:\\ProgramData\n",
      "PROGRAMFILES=C:\\Program Files\n",
      "PROGRAMFILES(X86)=C:\\Program Files (x86)\n",
      "PROGRAMW6432=C:\\Program Files\n",
      "PROMPT=(vs) $P$G\n",
      "PSMODULEPATH=%ProgramFiles%\\WindowsPowerShell\\Modules;C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\Modules\n",
      "PUBLIC=C:\\Users\\Public\n",
      "PYDEVD_IPYTHON_COMPATIBLE_DEBUGGING=1\n",
      "PYTHONIOENCODING=utf-8\n",
      "PYTHONUNBUFFERED=1\n",
      "PYTHONUTF8=1\n",
      "PYTHON_FROZEN_MODULES=on\n",
      "SSL_CERT_FILE=D:\\Anaconda\\envs\\vs\\Library\\ssl\\cacert.pem\n",
      "SYSTEMDRIVE=C:\n",
      "SYSTEMROOT=C:\\WINDOWS\n",
      "TEMP=C:\\Users\\PC\\AppData\\Local\\Temp\n",
      "TESSDATA_PREFIX=D:\\soft_uncode\\Tesseract-OCR\\\n",
      "TMP=C:\\Users\\PC\\AppData\\Local\\Temp\n",
      "USERDOMAIN=DESKTOP-N2RLOJJ\n",
      "USERDOMAIN_ROAMINGPROFILE=DESKTOP-N2RLOJJ\n",
      "USERNAME=PC\n",
      "USERPROFILE=C:\\Users\\PC\n",
      "VSCODE_CODE_CACHE_PATH=C:\\Users\\PC\\AppData\\Roaming\\Code\\CachedData\\19e0f9e681ecb8e5c09d8784acaa601316ca4571\n",
      "VSCODE_CRASH_REPORTER_PROCESS_TYPE=extensionHost\n",
      "VSCODE_CWD=C:\\Users\\PC\\Desktop\\vscode工作区\n",
      "VSCODE_DOTNET_INSTALL_TOOL_ORIGINAL_HOME=undefined\n",
      "VSCODE_ESM_ENTRYPOINT=vs/workbench/api/node/extensionHostProcess\n",
      "VSCODE_HANDLES_UNCAUGHT_ERRORS=true\n",
      "VSCODE_IPC_HOOK=\\\\.\\pipe\\ccdd4d73-1.100.0-main-sock\n",
      "VSCODE_L10N_BUNDLE_LOCATION=file:///c%3A/Users/PC/.vscode/extensions/ms-ceintl.vscode-language-pack-zh-hans-1.100.2025050709/translations/extensions/vscode.json-language-features.i18n.json\n",
      "VSCODE_NLS_CONFIG={\"userLocale\":\"zh-cn\",\"osLocale\":\"zh-cn\",\"resolvedLanguage\":\"zh-cn\",\"defaultMessagesFile\":\"D:\\\\vscode\\\\Microsoft VS Code\\\\resources\\\\app\\\\out\\\\nls.messages.json\",\"languagePack\":{\"translationsConfigFile\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\tcf.json\",\"messagesFile\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\19e0f9e681ecb8e5c09d8784acaa601316ca4571\\\\nls.messages.json\",\"corruptMarkerFile\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\corrupted.info\"},\"locale\":\"zh-cn\",\"availableLanguages\":{\"*\":\"zh-cn\"},\"_languagePackId\":\"4000923e07438a458172c6e7b57c9479.zh-cn\",\"_languagePackSupport\":true,\"_translationsConfigFile\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\tcf.json\",\"_cacheRoot\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\",\"_resolvedLanguagePackCoreLocation\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\19e0f9e681ecb8e5c09d8784acaa601316ca4571\",\"_corruptedFile\":\"C:\\\\Users\\\\PC\\\\AppData\\\\Roaming\\\\Code\\\\clp\\\\4000923e07438a458172c6e7b57c9479.zh-cn\\\\corrupted.info\"}\n",
      "VSCODE_PID=2824\n",
      "WINDIR=C:\\WINDOWS\n",
      "_CONDA_OLD_CHCP=936\n",
      "__CONDA_OPENSLL_CERT_FILE_SET=\"1\"\n",
      "PYDEVD_USE_FRAME_EVAL=NO\n",
      "TERM=xterm-color\n",
      "CLICOLOR=1\n",
      "FORCE_COLOR=1\n",
      "CLICOLOR_FORCE=1\n",
      "PAGER=cat\n",
      "GIT_PAGER=cat\n",
      "MPLBACKEND=module://matplotlib_inline.backend_inline\n",
      "KMP_DUPLICATE_LIB_OK=True\n",
      "KMP_INIT_AT_FORK=FALSE\n",
      "\n",
      "--- 总共检测到 96 个环境变量 ---\n"
     ]
    }
   ],
   "source": [
    "# 使用 .items() 方法可以方便地同时获取变量名（键）和变量值，之前已经提过字典的items()方法，可以取出来键和值\n",
    "# os.environ是可迭代对象\n",
    "\n",
    "for variable_name, value in os.environ.items():\n",
    "  # 直接打印出变量名和对应的值\n",
    "  print(f\"{variable_name}={value}\")\n",
    "\n",
    "# 你也可以选择性地打印总数\n",
    "print(f\"\\n--- 总共检测到 {len(os.environ)} 个环境变量 ---\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec64b87a",
   "metadata": {},
   "source": [
    "### 目录树\n",
    "\n",
    "os.walk() 是 Python os 模块中一个非常有用的函数，它用于遍历（或称“行走”）一个目录树。\n",
    "\n",
    "核心功能：\n",
    "\n",
    "os.walk(top, topdown=True, onerror=None, followlinks=False) 会为一个目录树生成文件名。对于树中的每个目录（包括 top 目录本身），它会 yield（产生）一个包含三个元素的元组 (tuple)：\n",
    "\n",
    "(dirpath, dirnames, filenames)\n",
    "\n",
    "1. dirpath: 一个字符串，表示当前正在访问的目录的路径。\n",
    "2. dirnames: 一个列表（list），包含了 dirpath 目录下所有子目录的名称（不包括 . 和 ..）。\n",
    "3. filenames: 一个列表（list），包含了 dirpath 目录下所有非目录文件的名称。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef6ba98",
   "metadata": {},
   "source": [
    "**示例目录结构 (Markdown形式):**\n",
    "\n",
    "假设你的 `start_directory` (当前工作目录 `.`) 是 `my_project`，其结构如下：\n",
    "\n",
    "```markdown\n",
    "my_project/\n",
    "├── data/\n",
    "│   ├── processed/\n",
    "│   └── raw/\n",
    "│       └── data1.csv\n",
    "├── src/\n",
    "│   ├── models/\n",
    "│   │   └── model_a.py\n",
    "│   └── utils.py\n",
    "├── main.py\n",
    "└── README.md\n",
    "```\n",
    "\n",
    "**`os.walk` 的遍历顺序及输出 (模拟):**\n",
    "\n",
    "*(注意：`dirnames` 和 `filenames` 的顺序可能因操作系统或文件系统而略有不同，但遍历的 *深度优先* 逻辑是一致的)*\n",
    "\n",
    "```\n",
    "--- 开始遍历目录: my_project ---\n",
    "\n",
    "  当前访问目录 (dirpath): my_project\n",
    "  子目录列表 (dirnames): ['data', 'src']  # <--- 列出第一层子目录\n",
    "  文件列表 (filenames): ['main.py', 'README.md']\n",
    "\n",
    "  当前访问目录 (dirpath): my_project/data  # <--- 深入到 data\n",
    "  子目录列表 (dirnames): ['processed', 'raw'] # <--- 列出 data 下的子目录\n",
    "  文件列表 (filenames): []\n",
    "\n",
    "  当前访问目录 (dirpath): my_project/data/processed # <--- 深入到 processed\n",
    "  子目录列表 (dirnames): []\n",
    "  文件列表 (filenames): []\n",
    "\n",
    "  当前访问目录 (dirpath): my_project/data/raw # <--- 回溯到 data，然后深入到 raw\n",
    "  子目录列表 (dirnames): []\n",
    "  文件列表 (filenames): ['data1.csv']\n",
    "\n",
    "  当前访问目录 (dirpath): my_project/src # <--- 回溯到 my_project，然后深入到 src\n",
    "  子目录列表 (dirnames): ['models']\n",
    "  文件列表 (filenames): ['utils.py']\n",
    "\n",
    "  当前访问目录 (dirpath): my_project/src/models # <--- 深入到 models\n",
    "  子目录列表 (dirnames): []\n",
    "  文件列表 (filenames): ['model_a.py']\n",
    "\n",
    "# 遍历结束\n",
    "```\n",
    "\n",
    "**总结:**\n",
    "\n",
    "`os.walk` 会首先访问起始目录 (`my_project`)，然后它会选择第一个子目录 (`data`) 并深入进去，访问 `data` 目录本身，然后继续深入它的子目录 (`processed` -> `raw`)。只有当 `data` 分支下的所有内容都被访问完毕后，它才会回到 `my_project` 这一层，去访问下一个子目录 (`src`)，并对 `src` 分支重复深度优先的探索。\n",
    "\n",
    "它不是按层级（先访问所有第一层，再访问所有第二层）进行的，而是按分支深度进行的。这种策略被称之为深度优先\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f036c46e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 开始遍历目录: c:\\Users\\PC\\Desktop\\python训练营 ---\n",
      "  当前访问目录 (dirpath): c:\\Users\\PC\\Desktop\\python训练营\n",
      "  子目录列表 (dirnames): ['演示1']\n",
      "  文件列表 (filenames): ['day24 元组和OS模块.ipynb']\n",
      "  当前访问目录 (dirpath): c:\\Users\\PC\\Desktop\\python训练营\\演示1\n",
      "  子目录列表 (dirnames): ['演示文件夹2']\n",
      "  文件列表 (filenames): ['day21 常见的降维算法.ipynb', 'day23 机器学习流水线.ipynb']\n",
      "  当前访问目录 (dirpath): c:\\Users\\PC\\Desktop\\python训练营\\演示1\\演示文件夹2\n",
      "  子目录列表 (dirnames): []\n",
      "  文件列表 (filenames): ['main.ipynb']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "start_directory = os.getcwd() # 假设这个目录在当前工作目录下\n",
    "\n",
    "print(f\"--- 开始遍历目录: {start_directory} ---\")\n",
    "\n",
    "for dirpath, dirnames, filenames in os.walk(start_directory):\n",
    "    print(f\"  当前访问目录 (dirpath): {dirpath}\")\n",
    "    print(f\"  子目录列表 (dirnames): {dirnames}\")\n",
    "    print(f\"  文件列表 (filenames): {filenames}\")\n",
    "\n",
    "    # # 你可以在这里对文件进行操作，比如打印完整路径\n",
    "    # print(\"    文件完整路径:\")\n",
    "    # for filename in filenames:\n",
    "    #     full_path = os.path.join(dirpath, filename)\n",
    "    #     print(f\"      - {full_path}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000dbfa3",
   "metadata": {},
   "source": [
    "介绍这个方法，是因为在你面临云服务器时候，往往只能通过命令行和代码块中函数来查看，无法像电脑一样在界面中查看，所以，这个方法可以让你直接在代码块中查看。"
   ]
  },
  {
   "attachments": {
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zu5zNrR/V9GDE+UTwFDj6bi2v3MpMdjhBYM7YpEYmbY6raUaw7qihblSm3pzISTF1UTPn3DZOq8Snhk8yJ905dhjp5m2FkmnnjePWF7ZPeTJduY8sI4AAAggggAACCCCAAAIIIIAAAggggAACBQQ+++yzv/iLt7761e5f+uVfFY8x8x9ssgaBkgVqmcy5N+1Xv/b1t966ms3W4kl5YMmcNueOMdgUu+WTsvi8bUXAlpMfONtmzh+xL1XDwF1npS5Hek7AIL1oLBY8skgsnOoic0+xvfeAYn2ZLXTbI8a0bIjdTI9Gndomv4CthGTO2zz3LDfjzt3TOSNum9XRiBPwHJ3LSUXtXbOzHe6bwZvMaQ+NwUgL/VEfuefWbhx1zpKfk1lbiZnMcmrm3IMUWBD98iRz+l13vEq3GNE+ghibMewO7Vjg4O5quTozv5DR3cxnQV3oc2fX87/tV65NWKWK+cmcz/FEMtc8ereA/UO/KynySOudIu5zNwg0T+eut24kd9K+htgtv8P6NZF1CCCAAAIIIIAAAggggAACCCCAAAIIIICAUZiwtXV15u1/8/t/4Dyddh4Uu8+cWUCgIoHnksxZt3FHR+fM27OaVt0H5oElc/rmVJsgPnDk6NhkOpPNrZLzfl6J3KtA4CQ28AZjYn2ZO+puMlHuPHPlnkj0VBR+ObVcoZbzueNYWpuL2jhvEFVovXsSsYHUTjH9mNJyacPd1ruQOdfsfFx6kjkB5fupKuWaUv7Xn/IeXPyU6HXO4lvyJTY0aujM6QmTNy72tx129vLWAur6qptxHhxclvYW4zrmDuwpbeRZ3Jzrcc+SO7akZ8O8H9Sls26dqNJy0a8mUdqnzGROuo7SQYosusc3QcTlK5bMuQVz216UIifmJQQQQAABBBBAAAEEEEAAAQQQQAABBBDYewJ/s7gYi7kVI+6DXBYQCEDgOSZzViDyjW/0pdPvVe9tHVwyp+vSTF0S/aGmI5394/Or2fyIseKAreIdn0cyp+sb7jR75kX1H1jSuMYiYAsimRNK3lzTezeJzKzCZE5MjSbFdd5z6PrK2aid8PmFQFp2dWYs3tHcdNANL0XKa91LXhBdF6OnynMQSmFkz3z+DZfbKv1hMubGcr7ji+bt4azQ3DnkQmGlsc879Z+zkfzdTc68UZm8iS6NZll2MiduHgOklGSOgrkcfH5EAAEEEEAAAQQQQAABBBBAAAEEEEAAge0Fnj17dvnyld/4zX/mW9TBSgR2LvDck7lQWPkn/+Q3L1+e3P79UNEWQSZzuq6rdyd6ogf83fc3D9wUIy0arRXRUYEcQmzgzZbE+jJ3lBILT4xU6ICF1rvWYgNvC90NjAVtod8d81AJRUaWtjwvix+eRzLnJkbeeeY2JruikUjBr65ptwivpGROBGk5ydxWZqZbwpEDOaXhSNR9KTeZk2o0GwbSNqEoTzw0uFgI2eVWU3Isd+5+CUmes68cQjf2JdVtz6XrrnO1krlHyR4z19zX0D25sX0yNzo92Ghp51wRp498RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEcgQ2Nz8ZGhr2jwDkp7ssI7ADgXpI5kJhJfxLvzI0NPzzn/88512w8x8DTuasBmmbqwvTY7HO5oP7peI54zJ45+ISsVaZAVvFOz6PZE5L9dsRiHMjRs6u+l+56iVzN/1PqOvZmQ7nGnlq5gptn7++pGRuod85iycHkudpU0L7G9qOjoxfTyZSq+vWQKiFQIxWiPntDp6xojm3CExx1uS31llT21hOLzuZa2g53n+63/9rxv/20ZauTixkrHxx+2Ru/IGuP1ye7O0clmdwdHj4jgACCCCAAAIIIIAAAggggAACCCCAAAII5Aj85Cc/iff9e2I5BKotUCfJnNXNvpdOffTRRznvhR3+WJVkTm6TpmYWxrpFNBUdW3Ffrjhgq3jH2idzj1KnnfESe86OtNjhXHT0nqsgLRQKogqtd3cVG8gZp8jMCmaB+vLAISczqzCZk2aq6026LfIuSPmfnMyJ66j4V56JfuXVzOm6Nn/Mfv9bFXLZ2Q6X1z+7chqlpgeOOL1WmgfSqvPC9t/XroqbubFr1n++QL/DlFkz5zTPSXPlT7qiVXfWuUtL5vzayToEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBf4Cc/+enJ3m/IDypZRqBKAnWVzIXCyomTsbW1TP6bouI1VU/mrJYtjTjTjIU7Z9whLUUwI+dJUl/EBt6xIgutd3cViY53x1onc9I4ltGxlS19adAZntH80W2vvSCa7Q2iCq139xcbyJJSHlZodMf04EE3+6kwmRPjNIaU+ILvuI7OQIvGu1RO5kSzj9145HZGLIjRKcNeEGuTrVTMnpeuYeCuLjaW019xMGdJXR5tdXMvbxGns0mh79m5YyJjbp0oPZYrv2bObaHPAslcoQvEegQQQAABBBBAAAEEEEAAAQQQQAABBBCohsDm5idUy1UpheKw+QL1lsyFwkrfS6eyWTfc2umbLKhkLrtiDl/ZeEjpuuZTgaTNOeVNgSRzj+Z6nDzpYG8y93xbmXERvTzPZE4ax7IhdsscZlBNxpwaNZ86NpFUeYOoQuvdqy82kJM5PXut272nW0aWc6G05eGoFPxUmsyJ2rWw0nLBJzdeu9DqNqNAMuftr90vMTplyDeZ0/XFMw3WkY9cyCR67b60TRd+e2iZc+LeKC+WU2/GK47lyk/mmkfvZgv9UbefEY+aOfe9wQICCCCAAAIIIIAAAggggAACCCCAAAII7Ejg2bNnzC0nHnE72QRrqidQh8lcKKwMDQ1vbfkWJ5X9/goqmdMSx52M53A88dDbDnV5uMV5Va5nEqVvnjxJ7Cw2yAnYpGqwsNJydll1NbSNmeNNIbuUyjipt8BIJBYtFzf8TuRtiWiAd727p9gg50S6Lo1jGTo656ZiorQrnDempQjYvElVofVuM8QG3nZurY5K2Vtj99jC/az6SNMeZtdSY11NSih8YJ87F2ClyZy+tTzgjNhpTCV40+2r0T5PnVlOzdy9sYj9KdaQs5euq0tnpTyvQDKnu0foONZjR57+5XdGUzyRbUPPtcIBngvrLKi3pFiuZWTJ00Vno6LfyxzN0nsdix7Z70Vxnxe4/3d4fL9zsg4BBBBAAAEEEEAAAQQQQAABBBBAAAEEXkSBy5cnq5fBcGQE8gXqM5kLhZXvfOdKIG/xoJI5Xb87KCqKlKaO/okb88nE/Nx4f2fEzX7CSo+IqHRdxFoFcgKxQW7upd2Ki2EYw0pof0MkEo1EGvaZSU/k7FjMCa4LJBNKlZM5aRxLpXNmU75YG6Kk7/Dgkpsp6rouAraAkjld1zMTLVJOmXOLNw4uJ/qc0LTiZE7XPcFVWDl4pDPW33+6P94RPWCcUekc6G+2Ty2PZqnLweGBI0fHJo17JpmYHrF2PHjILokrVDOne45gduR4skBFWXamy+mpc2/kaMg/em6P5RFxb2+/r/faOVeeZM6R4DsCCCCAAAIIIIAAAggggAACCCCAAAII7BqBv1lMR3/jiPzslGUEqi1Qt8ncb/7Tf7aYTu/83RtcMqfra1e7iwYYB9rGvAMqiuCt7GRO19WFfmfOtpywxJgALPV8kzlpHEul5ZJUnGddsQciLWscXBZXsRrJnK7rm8mBZjMh80I19s5lt6RZ4naQzOm6vnap3YpF896TxqCR6xedAjhPMlc0ODwcX7gWd47mH3cZNXnT7c42SijcMFDwTSHKyKTtC2Z1nmROXJeC20vH9G9qVZK58hpWSuO9EwGKW5MlBBBAAAEEEEAAAQQQQAABBBBAAAEEENhzAk+ePOnt/Yb08LO0Z4zeB9HsjkC5AnWbzIXCysneb/zDP/zDDj8LgkzmdF3XHiQHWt06J/EuPdgan7ybNwLgjpI542zr84MtzrRtxqXdH+0YSmaNKrTnmsypqZg7umPrxLrfJZKmXmsaSDtVXiJo8aY7hda7RxYbFMg4dS17Lzk+1N/TFo00d8b6x2ZW7cvhJkahnSVzuq6rd6diORHgodbhlHGigsmc/z1zIHJ0whgxUvTLC+J23Mgdp9rcD/pDg4tyDaK8mU4yJ96PxT4Hc6JTjyE/IIAAAggggAACCCCAAAIIIIAAAggggMAeEpiZebvYs0T3wSwLCAQqUM/JXCisXL06s8NPgYCTObs1mprdWF0wRiZMrWxkVSd42mFbC+2uqVnzTynnEfGMpyiq0KH3wPpErxPY7DiZs7Q0NbuWTibm02vlXHlN3VhJmUNZpjPqo3Lct1IxZ7jOyNhqOXvWdFs3AfUOr5rTBjdRLpSwercXyaVzEXf+8Ucy5zXmJwQQQAABBBBAAAEEEEAAAQQQQAABBPamwKeffvqv/00HyRwCtReo82Tu9zv+7cOHn+/kY6E6ydxOWlTdfVdHI3aGsZeSOU0rmHUJkIPyuJrVvQqBHj096Mw4WFqaFejJSz9YVZI5J5S2oulg/vuwykF66WRsiQACCCCAAAIIIIAAAggggAACCCCAAALPT2B6+q3aRzKcEYFQWKnzZC4UVv7iL67u5K25x5I5MX6m0jO3BxKILW1tfqQjeiB0OL6QN5ioMf7krbg7NWDP/G4EUW8cdcrF2qeyO3krVHnflav9p/uNr5lidX2rM+Y2p/vPL9RzZ6psxeERQAABBBBAAAEEEEAAAQQQQAABBBBA4LkL/OEffo2UCIHnIlD/ydy/+8OvPnv2rOI36V5J5rL3kjcu9re507+F2yc3K0bbPTveHXSDt1BT93g6q7nTsD3KLl7sbnTGgQwdHlxyX6r//q2mJufTK/dSk/3NzudCQ+zWbkwW69+aFiKAAAIIIIAAAggggAACCCCAAAIIIIDAnhP4679+N/xLv+I8fXWqI3YymU60c/hKaimTVb9wHuRqmrqZWbx+/nRrEMffSdvYt84E6j+ZC4WVVOqHFX8u7JVkzh1L0PooaexPOe/+iul2x45rF1u3//RUWsczu6M7ViuXBhtyOtXYl/KrCdxNnaKtCCCAAAIIIIAAAggggAACCCCAAAII7CGBLXVpeqSnLRqJtPcMTSw8cJ7XbiZPGyubO3rHEhlnZUAu2sPsWjqZmE+tbGTV/GO7c8cw4Yuuj7z+Rs4z2B38GB24nlGfFr6KT7X19EQsauRzk/c07QtN20gN7CwraumOD1yYvXF9bvJsvKcjuoPGVyc1jHb29MVj5lfB5rV2WxvE+uJdtQovu66sGlfqqbpypfs5ogWQzLV2x85OWTdAlfRGXn+j8D29zSt7MJk7cGQope6i+rBtruD2L2dvDh7ZX/DjY180PuP+b2/7g9XDFmJuPOujobF3LruXLmg9XAPagAACCCCAAAIIIIAAAggggAACCCCAQOUCaup0U84Ty6aeOXt+l6XBJicSONB2MZB0TlufH2w5lHPGA5GjE0sPpU7citvnbZlYl1bvwcWtra1/1fZ7zlXIcSvzx+hgQn7+rGbX7qUT1+duXJ9buJvJyvUW6upktzJj1ZCo6eEKk7lo7FJ6/Yu8i/Ywc2OwhCKWCk9apolxlvNLbiM3kh1+5x1OuzrZhVMVnKKCXQYX3XdENhXza1Uwd8V2R95RMtc9sbiRG7yr9+cGgk43f/d3/9eKB7TcK8lc9l4yMZ9MpDPqo7z35F5YsaWtpadGezuPRKIR66u5MzY2tXjf51dD6t1jS11JTY2a87ENX0yubOa+x+q9/bQPAQQQQAABBBBAAAEEEEAAAQQQQACBvSywlRm3H5EfiHTGT/d2Ruy6gujoPdNlc6pNPLhviN1084nK1NSFfjfqy8sqDscX3MOTzDnA77//fjABTHRkwX1+q/pnYy2npkSOomXWrFmoKkzmosO37XzX6Yr8XV0cqaPiuXPL7pPtjURn3p0Z7l9wu5JNnRbviPwtg1wzfNt+P6i3R4K5BypqeeXJXPfsmusqX3xd183oN9hO3fvgg/WdmYUAACAASURBVJyTlPjjXknmSuRgMwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEqCtgBWJOI3NRU7LCZLhxPmg/VM+eapbBhZxVs2q34QScb2Nd8bHQ6mZifGj3avM9ZGeqYtRMQkjnnqs/MvB1EgBEdX7VDEnV5ossFdwZylMYYbB1NZT15SmXJ3JCoMtM1Y+TSG9eTi/elI2ur4+aYmUH0TrpF3a6VtTAmorn1+c7cJp1KScFcf+6rZZ2ovI2jHf1jo/2dkfL22rGG93SVJnPdiQ3nJta17L2UUZp5383ede3+rG95YsW8M2/Puucra4FkriwuNkYAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoXGD9ojmoYPP5NekYaxeajYfjkbEVc+VCn/ygP74gbVnuYqLXOVT7xLo0J456003sOmesDCQvmbMnnisy7dyWMzed3zbaw6z1xxq4zNo0d1C3R6q9TT0N9vbaa39ccVbh7hj5tp08aZlZEcuFlZCTn61dcy6NkcpEpcEbdb2iZE4c4WlmUhq6MDIiEruVS/JJjeWWbnu+Nykp9KyPHc+NqSIdx5wZ4LpbPJFSa5c9e1zO+tyTmkpjRQa0jN0SwZz37aCE3PnnuvPH54x2HPd0x+pdzlR2ov15R3A1YnkvGW0u59QhnwjW18FnZYXJnCfsdOfJk2+tgMcFfe21Py73E8nanmSuMjf2QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChboLbJ3MZ4i/3cP3J21dvW7Mq8OQXSfHrNSsXkZE7LTHaLMTD3RY/Z85+5h3i4PH40Kgrvwkpof7Tn4rIqwj9x6tgtdXHIrtJruegU9WwmB1ob3BwrFFYOtnqnZHPPVfOFr/9Rj9ywipY7bzww2/3F8rmcMrWcZO6aNbOct5MVJXPiGuXO3NY5PG3MbHfj+tw5MWFb68D1jPrUe15pOrrTbtHa09VJTxei4kQP5pwarO7x9IYmH+2ptp6eiHl2zI2gRJSo5wxoGZeHshTzveXPoPbFxuKluFTfNrLoVIitXeuevOcMTZm2hqb0m4Tvqbp2fdA9guhaZla+7pEzcytS8aGhVuzU0dj0qmyr3pvypLOeODOXJRRWKkvmxAChXyyfk08RnVpxLo1qU/icVO5vicvdXz/qvYFK/YlkrlQptkMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYqUBtR7MUNXNK6+hdJ7Xw7YNI5kbO9XoyM+MxvTwj3YPZLmv4Tfnpv7m873jSOYeUzJ0ZdEfUtJO5zESL4pcNKE2nbzkH8G1kTVb+VsvvlJhMFNysz8611ufyxmmsWjInBV3a+s1BbzVbjnbnuJNa5YmqS982p6PrTK47r61ckSaok2Iep3fdMxnPYJzOfrqeTQ8XCeccCl3XPQNaOnq6rmfd27LwDGrZ2yNOtCaSufVVEUVbcVTXtUKt1N1Z5XyTObnoUHTNXPI/dVoUKbrbq2m3kTnXwufHipI5Kc5cnci5M0W/clNbn7Pn7Fvkx+Z/3uJ2sKwFkrmyuNgYAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYCcCG+P2SIMHIp3x072dkf3Ww/Ho6D3zsJtTbSLxaortLKnSUv2N4mjKvob2nqGJG+mMNcKkpxtuBKIoIaWpo3/ixvWJWPMB97m8U+6mJY47T/OP9CfuG6NRrl075pylYeCudVSRzBlH2B/t6O0/3d8/PL+hby0POMFeY+/UykY2u7E602+O52lEgINLovDO08Ca/fDrv/4/ur2ubCFy3SoNzCkFM92cOMoezfLUeauazfPf6RGnFs2hli5iwSY5R7ahnqrZzPLC1fOnO6RczTqOGPZQy6anBvriscGppYcOcDZllqk5ZX+6rq9OOdGXErniJF5OLd1pd+TJp9mVq2Oxvvjo1WW3wCx7q8gscf2iNk5KjDrmncJKUUsnbZldnTkbj/WNzdwVJ1mwawFFMmd05qmWzWTW7meWrg+GwmLwTO1BcrS7NdJx7JyY3i8zYyaIIsFya+aiEytu7KhtLM1PTV5JLm2KVXaQGfaeWt1YSs0t3HNbqOu6fYqCl0+6xBUlcxNFCuNEaltROWahNjcebnJumvK+k8yV58XWCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjsSUFOnm7yJi9IUm7Mm1tIW+tx6tQNtl/yGOizz3GtXuxt9CtQORI6OLW5Kx3KTuXBD7KZbuJadbHea2peyttYeZtfvpRLXk0vuVrrI4ZwAT6zx1NvpujZ/zH7Q3z7lTiam6+JEPfNu7CE1r4aLv7rv1wpFESWut4MQ3xTEyc+888w5yFI8U+K55M26rngGURRmDzOJMXfiMSXS2t3TNzZ5fS4xLRVyiXE1MzNmMyLuGieEC4WVcSeYc+I6kXjJpXUiwPtiebRwp8SYmSKE60yIYC5pJ5RulCi1JBSOTjqN0e6OmQ5SPKZlZrpl1Vl3Zse1aTeqNMb5nDwbd+fYy0/mpJhQmqQtKp0oM2vGltIaVVQKCgddXRyS21NsuaJkTuqgZwpD40QimXu6Ol74csj3UinLv6zsE/dYOUskc+VosS0CCCCAAAIIIIAAAggggAACCCCAAAIIIIDAzgW21JVrY7HO5khzZ2xsVkxhtZkcPdoeiRiVbQsPgguoHq7ODHUeOZQXBsijR4pkLp6QqtbsifHCSqhlwh3e0AXQ1Gw2m1m8PuKOTpmfzOVMcbfQbzfjyNllo+DO+bN01i6bOzi47B7/uSyUkkkU38YOeIomc2UNb1j8dJ5XWwdnfGsidV0aelG6E6KdPX3xgQuzi+J+s5O5kDRw5colaxdRmGWvGXRHbtxI9MVj4ssdDDO70CedLicWOuVOZ6fbY2N2zrm3mTvE5cBtJwR+kJROEY+51XV2nZ+Ix7Tl8x4WqWZOf6qupZMzFwZ78qoJ85O5mfvOPfhgzi0cDIUVkdjZWZc4tXcut2KBmbeFQqmKyVzOFHQ5l6P8Hx2d8r6TzJXnxdYIIIAAAggggAACCCCAAAIIIIAAAggggAACu1RAUzdWUlPDndF97iP4Q/GFR2ZvpGRuQeqeXzKnrc+P9bQ1iIO4Rwsr+cmcs8Y6qDuYp8ghcvMJpzhPakXtFr/88stfO/Df5TZJ6mApL51bNlNV32QuOrLojBtZrXDObG2k49joleRixsm0DEJRthXpnVi4n1Wf+sI6yZynQs6cuuySU6TmVsJd3basU5zUj06aHc0sPhOFeqKKThHxmG97jZ6lh41eF4rHjJvNf545TV1Lzw7YA8wqecncoHyxPO0XHbe4Cp26Zsnc+aUvbB1vNGj0XdTM+d6TZd7ersOv7vu1Qhek+HqSueI+vIoAAggggAACCCCAAAIIIIAAAggggAACCCAQkIARA0yN9nYeaXKHrDQDqkNNkbZjwxeTonhO17XM1Omj8dHp9JrPpHA7bY92/3yL8zg+dss8WqnJnLZwpsl9NB9SGhojzR29YzFn0EsnhxOjWTprrDaL9aFDTZFI1OdrKL3T7u1s/8bDUgcdJdHlEtbYNV6FRg4sGM45OdD92bJOV3zjSO/cupPA2ZnNUCrrrDGoNE17uLGyuuEUaYpkLvRtK2PUdbMvduKo687okUrIHfFS17UvNL+vjYXBwilsXvGZewr9wZw72Z4IzJ76nkLTNlIDxnUpFI/ZDWgZnFvZVDW579at8tAef1KcyJ5nzrkiRvY34nGuu2ROyjhz75/ojQfOW8Jb+efpUQk3ds72/7ih0Tlued9J5srzYmsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBcgW0bHqyr/Wgz3xvuaHFvuix0ZSTkWQmrFEiD7YOzqzKxU+lnf/BhBO/HbthFcaJ/ZaHDztxxSVzXq8Sk7nsrJOXNPVcc9qp6+6IhU4OJxI4Z419bnfLIxe2LbcSza3l0m//9r/ISSDK/tGuLdOWxnKvr30o33Cu0x4BMiuuRYHdfUIUtzZLW/p2zl7dCXdCQTOzEXPFPVwe73UmXXMmwNN1KZkTg0BqS992cy9pxjWx18aNaM55S/vR6bWuy6dwBrc0eypKvrYJltwW5gVpXrGW7vjoleSSuH/1tWuGQ14yJ5XrbTiT3pmHit1yJkmsl9EsldG7TrTqmY1PkUclrejWKngdf6vldyp7Y5LMVebGXggggAACCCCAAAIIIIAAAggggAACCCCAAAIlCai34o1SMHCwqb2nf2xyPplwvm5cHIl1Nsu5XePxpPXgPzvd7sZCjf1JVZoBbvtzb6ViThbYMrIsJ3tyk06nzCOJNChebDTLVL/dHnnaua3l4Yj9+N7J4Qomc9mrnU46NbYidSd7a2x0Orl4P1uFEsHtqeQtur9+1DWvdMGZj211Sp6czHM0J5zTMnMxM9Ny8icp95JuG8++PutFKKVvJAekkCwy4k4Fp6u3B0NhUQe2dk2ELl03najJk8yJgRCzd5ftLbKp06IBTk91fX2+WzQyen7hXjpx9fzA8c6CAvZBOt2KLnEKfeNGp2hbyB1FU99IdIv1kQupFc+McQIht8Tt1Pkb8+mV+xvqRlJqvDQCpCHjk8x1zJm5tXF/SNclKk6km4NwFi7XcxNTXdYWUEJS9KuieeaUkEhJjWDSYW8dveu+9aUu+J23eKvyX/3q174uv3FKXyaZK92KLRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTKFHiU7LHisf3RnrHUWm7tmnS0LW0tNRFrPmA9Ae+ZNytgHs31SM/QndxL2qvo4tKgGJhxX0N7T3//6f5+zxRxhweXrHisxGRuw63Da4rNZ7UtXVMzM8fFWZwWFkzm9Eep0065XmPXWOLeRjabWRjrbrRDxIaB5zyYpf7aa3+cH0KUu8aJ2bSlbztFadJ1tI8WHVlIz3ZZ67vtgjl5FMeyTnrareLSdf2pms1k1u5n1jbdVEZkS2Latofpc92toWjn6StycCvXzCmhUyk3srPutfV5J1s1Wz582z2FupaaGu2LD1xIrrnrPDGeCJ/krknpl3M3S0NZmluKyfl0NbNwZSzWN3juZsadJy9rB8YiMMtN5vrsqNuYk+7+3Ghf3DiCW5yq29WN+TVzoejEilOKpmsbS/NzN64nlzbFKqcsstCpa5jMhTtn7rsN07WseQNkpTWrE05c538h5ItSyvJrr/2xc8HK+04yV54XWyOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiUIWBnBu2T7oiC2+ycnbTmbOuzatlSMTnRkSvVtjmO+fJWZry18FN4pXnAracqMZnTN/wO2NrSYp9l+2TOmLJr8IhTzJcTADT2pdxMp5T+VWObmbeDmObNTXS0jcSZwuGcHctNLD20uqIuDhW+XvKd4LPcPZMRMUyejJa9ZddRdVz12051ixW9yVxYmsDMOKi3mi2shKIjCyKpyjmtujiyXd+NI4j4ytrfGltSvjciIykpY/KexZklrnDhmhIKR52s1Luv+ZN2385HfZK5sCIXHebsnL3tlqbVQzJnXItF+0bKaamuZ+259GTVHS7PvD2bd5qSVpDMlcTERggggAACCCCAAAIIIIAAAggggAACCCCAAAKVCNiJ17Eb2VL/3DhuZjOBJHO6rm+pS9PxlkPevEdpaOmbctIgs1ulJnO6ri6Pd4siudCh1uG06s4eV0oyp+u69iA53BndJ8dLh1oH5qWJvyqxDmaf//yff7TDxMLaXSQ6T7NLl+KFypVaBpPrdqCmrc85g4XKMmUstw5cX81+keugba7eGGyVOhUduC4KzozLsZEaFoNe5iRziqemzR680Xs7RePjKc8BzWOmxSR223QhKiIxo+2ZGWk0TrfZkd6JhYw3t32qracnrLFAzc0KxWNWa6M+OJq6dn3QmY7RZzRL+1KemVvJSR+/2Fj0XNNCpxahY9VHs7SQo/Hx9Ib2VLoHcpW8126bS1Nw4/Av/cr7778vnaaMRZK5MrDYFAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKA8AZF4FXzG7WYPnoWgkjm3uY9UJxt0q6Pc18pfsI+2s0NpTpN2dpjyW19sj2fPnv2Lf9nmuRaVphddV1bdKEnLriYuDHa5JYzRzp6zs4tS1CQVYJV5q+Q3r7U7ZgzYGI/1HevwS7nM3rV2mdv0dJRQ1pZ/Cp810Y7j2550x12LdvZYXdt+Bjv/c0U6jlk4ZXfcUS17Rx8r/7ZZd12F88x5zuJci0qVtr3//1Xb7xV7FxV9jWSuKA8vIoAAAggggAACCCCAAAIIIIAAAggggAACCOxEoH6SuZ30Yo/tO/L6G9smEyVuEDkzJyZdK8RYtKiuxBOx2QsjEEQyVyz5CwRq5PU3Ct3O264nmduWiA0QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEKhVwy8KcgrVSv9tlZN79HxaZSKzSFrJfnsC7794OJL1wDuI/yKRx2qfqWnp2wC2k85Q9VT1ccZrHiepLYFckc3/91+/mvW9KXUEyV6oU2yGAAAIIIIAAAggggAACCCCAAAIIIIAAAgggsBcEnj179tWvdgceXLV0x0evzN24bn5dPT9QtZEGA285B6ylQP0nc3/4h1/b2tqq+KOAZK5iOnZEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQODFFPiLv7hayzCGcyHgCtR/MvfWW1d38rYnmduJHvsigAACCCCAAAIIIIAAAggggAACCCCAAAIIIPACCvzd3/3d73f8WzcsYQGBmgnUeTLX0dH56aef7uQ9TzK3Ez32RQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgxRS4OvN2zcIYToSAK1DnydzMzNs7fMOTzO0QkN0RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHgBBf7hH/4hFou7eQkLCNRGoJ6Tud7ebzx69MUO3+0kczsEZHcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBF5MgcV0+p9Ef7M2eQxnQcASqNtkLvobR957787O3+q1T+Y0NZtVtZ23vJ6OUL99qt+W1dP1211t4ZrurutFaxFAAAEEEEAAAQQQQAABBBBAAAEEENjdAt/5zpXwL/0KoRECNROo22TuO9+5EsibOehk7vZIJBL1+xpZNNubvdppXLzjSSubWxyKRiL2S4H0p+yDFGxw9+RG4YNtTHVFol3T1hbZmQ4lFFZ65muUN2qpwSORaM+1rG/7tJv9kUi044rRthxt3+3raeXGZFfuzdN2dGxmVa2nRhZuy8Plyb72xkPGzRAKHzjYFp+8G3zLd9s1LcxlvrJ0ttn4uBhMbbOd+7Lnreeu9Sw8/08VT3OC/SE9HIlGuqbWfY5a5CWfrfUSJP12Yx0CCCCAAAIIIIAAAggggAACCCCAwF4UGBoarlkqw4kQqM9k7j/8h9eePXsWyPs/6GTuljnm7KGmvHDOjt+01GBk/4G2Sxmr9Qt9SigcXwikK5UdpGCDiyZzDyZawkrLRSuZ0xYGo/v2t4/bfbLipWrGjY+SPYoSap/yi+a0xHElFG6f3DQ4crQrE6rhXhvjLWamJcLdpoOKEXQdGVquUexZcW8zE237jcZHOuOn+/tP93ZGjB+Vlov2bVHKgdenuyOR6PBtaVszOXYyYGP9brumUl/yF7fSA4eUkKKElGOJR/kv+63xvPX8NtD15/+p4t+uQNamYmEl1DLhl8wVecnv1CVI+u3GOgQQQAABBBBAAAEEEEAAAQQQQACBvSjw85//vO+lUyRGCNRGoA6Tub6XTm1ufhLUm78qyZwTWW3fyOf/DN1M5kpvsN2lYg+1rXipunGj6dY6/iBP+NFcT7hQaJe3cd2t8KPTMudalVA4Orpad82VGpSd6VJCSuu5+1KAWH7L1y+2hsJK7JZ04MruT+kA9byozR8zruz0WCSs9MxJdEUaXeytZ+/2/D9VirR/py8Vid+KvOR31hIk/XZjHQIIIIAAAggggAACCCCAAAIIIIDAHhX46KOPTpyM1SaY4Sx7XKDekrnjJ3p/8pOfBvjOr3ky9yizOJ9czNhP4X2foWuZdGJ+7sb15MK9rLYVYGf9DlVi8rGlraWTietzN+bT65quex9qmw1Orz3S9c3VxPxULGJUrQ3PJxPzq35lbX7NKHddevBgWImM5aZV2twxY1xNN+TwatsnMVfeuD6XSK1mRaGStnY7t8HZe8nEfGpNHpFxczkxn1wxC/LKbXIJ2/slc7qu3zU6e+SCXXxmtioHNrsyn0zcs7HNy2FuYPTUuZFKOP0ONjFDkd5k7hHuTZ3u75/Mqffz9ze6MNkbDYWVtpGkiWxekZH2UFiJ9E4l3HeN95oaGreNt5OWXV0wO+u+uTyNcU+azmhburhjxUZa9l7KvMPN951YX70ls77z0OCSvjoaUUIds/5vlqJvPatxbt8XzIFPfT9VCnfD7Lj/fWJcFNPTxTHf5rnHcl+tAV2R+M33JdG2xfve+T19PsQ875qEeavk9pWfEUAAAQQQQAABBBBAAAEEEEAAAQT2sMDaWobKuT2emdWm+3WVzL3071/+yU9+Euz7vubJnPdxcO4z9K3MTHeT59I2xW9UKwcyJUtJ5jbnYk3W5GHmf/e3j988L41mqZulTmYFm3k0qf3Vq5xbNoYBjI6teG4Hc8Y7eWBAr7au62tXuxvN8SHtRipNsTk7EFk5Gw2F5To8KydTOq6KxGRpsMEdKtNz5mB+KJDMmb0I9dlTkeXeM8apzUzC2cC8HPEbd0eOSD3d1z6xVsWUN3Vayb8cPiiF/c0uGBPU2V+xW7a/u8au7PReU0OjZWLhWnejs2MorDT2p+Q4NTsXly/6vvaJxIVWz7VW08PNB9wThcJKlblMGbO+8+CZtK7rBe+r7d56uq4ujbXv8/Z9pvQxctXl0XZvx5tHFoWdcVFaLqZmuqQPJaXp9C2xhV5rOt/4zbrT8l7yadvYktt2741kvWsS9qCszn3Y1L/gbu9zO7MKAQQQQAABBBBAAAEEEEAAAQQQQGDPCWSzWeackx+lslwNgfpJ5oaGhgMcxNL9vKirZE5b6G8KhZu6LqazRk2dlk2PtViBR/UylW2Tua3MuRYlpDSfnjeKjfQtbe3aMSvncMfAFMmc4VogXnLJA1pYGYsaIwHekw6Xne0IK6HjSTEsoPfhu5bqbwwrjV0Ti47vqDVQpHUQszRN5HDG0RoOHlJCohTMrG3yn+NKakbliwXozALBlkvWrH6+s4iZmYQnmYtGWtpHrfvoUWbmuJGsiK5V3sKCey4NGqdo7BpbcOpB8zfdxl+3It7tRrP0XlMjmYtEj0TjM+Z5tWzavKYNA3ed82eMFDl0pD9hNczSMDJLkcIunmkIhaMDt8zbQtfWrx1r9KvIdI4YzPfs1c5QuGHACObsskj3DWWfoIS3nnozfjD3lj5gTFxX0uyVG+PG/d/Uc815a8/3G2luqxviGvdVJNoc6Z01KmLdTySjzs/+U3O6vPjNaYmdT4u3p9m7nA+usBI6Omdnbd4byfwQO7Bvv6RxzQx0W33ntBNnZQkBBBBAAAEEEEAAAQQQQAABBBBAYK8JbG1tXb48+U+P/M/ViGQ4JgL//zBy9ZDM/cZv/rPvfOfKs2fPqvEGr0oyd7D12On+fvlrxh120fs42FP/tDnVFlYiZ91Njf5mp43R/DwzbwXLYFW5HWqKRKKeryErMdB1v+jOfByvuEHCc0nm9FVjdq6Dg8uuhyfqsNZ6tLOT7WZdlxxzmuZ2OdpW2qjDO2oPhWkOjHls9GxryM0hzKPlD6HpNmDHCz7JnJaZ7TnsiZE894x9yvxkTulxAghjE2v6PSe623E7/Q6wpS4MNdvFW4ea2o6OTeYOBridf8XJXE5Ae8+4Mdyb0+QSIZzRdOtCi2TOZG8+vya6pa7f3/AOfSheC2jJ1Dg0uGjfjWbom1MDuv1bz2z5ofiCGJRV161bupRkzhoS1u8Dp2feSrfN+yo6tiK9ZbylpbWnM5ukNDTmfF4ZPzYYt5+bzJm987wLdN384HJuBs+Hg50KWyWM7iU2O+ukp+5aFhBAAAEEEEAAAQQQQAABBBBAAAEEEND1dPq93t5vECMhUA2B557MxWLx9967U703elWSufwrIaI17+NgOWUxoyBvEZhRgTbRkhfXBcmxXTJnjrPXOm7XazlnNsvL3PDj+SRzVnGeyDZyog6zqbK2mU7lBJ9GhV+rEorYo2Ial0OJL5g5hLHcMZs1Yh77opjJX94FckiC+G4lc85IemKIwqauq/Ykc7peYs2cEz/Yzcqca5ZCiyDa6nsMLbuauDjS02ZmJGEl1NQ9vuyMBliCv3kjeXPo/HRKvqa+GlvJWFhxBv80Rz3Nm8LNvqsfWJ0w53uzalXliMu3h0GtNHsh50D5NaDbv/WsItF+e5hTp2nmmK5uMpdNnfP+lsDpb6es4Vm9GZu7t1l4ah/Tk/jaW9yMS78rUHu6UpM5s3fHbuRc0FS/mIfSeyOZ955Uamn1NnP+SFU/fh11viOAAAIIIIAAAggggAACCCCAAAII7EYBTdNmZt7u6OjMTwRYg8BOBJ5jMvcHf9B5debtL774oqpvyaokc25k5dN07+NgOZmzYgn/q1W9aqf85MPbaLmF4hVvL55TMmcVFDoVLWapkFxCZ7RWbqe57M/rxBja/DFjgEFjIEQj0TGvozGDmrmg3TiqiPo5YRHgkpXMRTucKKUjagw2OJAWw3NWmsyZR3bLiQJscqFDadmVa4NH9ishxckIS/APJpnzzLrnly3ZxXlOw3RdnixtX0NzR//EwgOPeaFeVrzezOGUtpFkYt75+nb3vrAiZ3Xbv/Xk21tqimfHjamunPKyrql1c2PPZmJ3U8yuHPXTMz8xxK8aSHO51YTObJL/nex5yexdfshtrLE/n7163g8xl8NPwH2R5mBxfQAAIABJREFUBQQQQAABBBBAAAEEEEAAAQQQQAABBHT988//n7feuvqHXV8r8PDZ/yEVGyNQROC5JHNf/Wr31aszn332WQ3e1vWWzEVjV5wn9e4j+/lk4p5V5VIFkN2czOlmzZAVZpjDfuYVtMkP383lSO+UyEKE8Krt69Z1GbUy1tHMqqCO2exWKqZ4Bs+swsWwkrn4gnNo7ZYxi9jBvpQcE/kFKp78wC9jqHkyZ3VheSQSVo5cMAv+SvCvVjInZgq0Zf2IdC2TvmEU/DUdNOZpa4rdcqr9nMsR3PfVUSNz9fsSNaC+xZE+YXP+7wH43SE+bS+wmXkv2WKe+8o+RE4yZ66tIZ3ZpFKTufZh8R4Xn6uL1oyD8odDflhr9bYGw8D6XBlWIYAAAggggAACCCCAAAIIIIAAAgjsPoEvv/zyhz/869ffOPt7v/ev/R/9+T4PZCUCfgI1S+Z+WdnX9rvtr79+9t13bz99+rRmb7w6SuZ0zzBxtRLYLplbu9AccoZzdNtkDrzplJ7kPtTOjZfcvaqwYMZmRpjhzGGWcw754btnkMOc7dwfzZEAO2ZXrna65XFGZ5X4wrIxe5koFXL3CHIhn84amdCTOPoEKuaQp874jdaMWVJBmNHC6iZz2t2p0/3943flANF0Mf3tQsYS/KuQzJkdl+Ius1lm+aOYZy7vEj5Mxg4podYJq7Ys7+UdrzBnwosMpbPeP2sXPZNKbv/Ws0idmRGdZplT1jlloM5Kn++mtufWMjayAlR78rlSkzlx9GrTWQWRJSRzpl7Ou0A00+2pm2v63Hu6rlvD9l7KGczXexx+QgABBBBAAAEEEEAAAQQQQAABBBBAQBL48ssvP/hgZebt2dde++Ov/1HPP/9ffrvx8K//8i//KnEdAqULVCmZ27f/QMN/f/i3Wn7na91/NDw88vbstffv3ZNu3tot1lMy9yjZoxh5wJo5z5ltcHesrbN/ZjUv8wiKaLtkTl81EqmDvUlRQLSVOdciDQqXm8xZsYco/Aqqpb7HscefnDMmx3Ifsost5WRON2M8pXVcTNmm61vLo22dp68JX3MyuWOxXneiMt2szIv2HG91p6ATxw94KT+Z03UzxTFmvHPOZU6g5Yzhaa5cu9BqvKWdIU/9CsKqm8xZN0nuratra982GtYzb9292/tbia8n/jS777mynmvqW1jmyZPMC6q0XLDqpAwv9Wa80fg1BCe2eZg+d7S97cKqA6zruhlu+cc/0laVLi6eachPu42DmSOyho4n7Xf79m89kzTcek66pdWbRp1lqIRkzuetrasLfU1G22wMj6TdXblmruZ0esnJnNW7xr6U+ODS9ey1Y21Hzy88NLvivZGsZE5+o+m6agxgKzQqvd7shwACCCCAAAIIIIAAAggggAACCCCAAAIIICAJ1FMyp+vrl4wkY1/z4GRqdX1jdWG6/4iihA7HF+Sny1LrA1g0n7MfbD122pnbzFk4v2BnQdbDeqWxeyxxb2MtPTfa1dTY2hqRkrCcKGhpqCkUbuoam0vML2fllDGA5uYdYis9cEiJRKKhcPvkZt6r3ofvRj2QooT2Nw9Mp1Y2NlZSU6eP5I1baO5i5ElzbhpqBWZKKLc4Ke90O13hl8xZgWK4IXbLaY8Z2ISU5tjFucT83Hhf88GOzrbnm8zZiY4SauocnjaHDbw+EWttMPJCOWne1n95pDGsNHaN3ZhPLllXMzvbpSihI/2T88mF++bbwHtNfSoIPfPM6fpWZrz9gPG2amjv6Y93HGkIKZ0DZ1pFMqdbhYlNXWPJlY3s+r3kaHeTMRtZlSqlzDs2FB1b8blbrJYcu/HIem37t56ecW/p9Jr1ibG/tcUIzkuKxpcGjZ5ab+31e8nx3mbjR5FmbZfM1ZjOUDGb5B+a5rykLfTbvbuRzmTvp2+MdRuJrHtDem8k80Ms2tLa1NhlfNC5t0Fjv2csWZ+LxioEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBcgTqK5nTdT17c7DlkJiA6mDrYCI/cCqnh9tsayZzfkWUTkWRrutb2cSZ5n3OaKeN3bNrmYmWwsmcri6PdpipTN4wmNs0pqKXzQokJdQ+5VaVicN4H74b6zeTA1ZiZHXnUOvAzZz9rHisc0ZabZapyVmdOEOgS77JnD3AoJHlODGnfJM0dk8sqZ4EJScoNVtoHtk/zwiqB+rSxWOR/eLWDSkNLWeS606eaJ9mG391aazTnOZNidgDKurZubh9WCsZ9V7T7ZM54wZWV66NdDRHI5Hokc6RxAMtl0hdHjfTOPuNsD/ac3G5Wmm4+Y6LjMkleuISWFWDbdPOzbfdW88oAVye6Gpy2Pc3D6dVPxNxCu+SunSxu9GYV8/8UhpahlKqc5vZMZhTi2nvKNfMmaevHZ3Rgpz4Te5N/ks59+SByNGJJfe6em8k+5bI2DmuCXLgyJlk1X+3QO4BywgggAACCCCAAAIIIIAAAggggAACCCCAwB4QCDqZC4hMe2jMQKXapTMBHXSHh9FUs005SUvhg2pqfbVfbukjsy8PS+6LvG/dLGtqVq2/Hli3bjaraiLgySPbxl9T8y6N9jDIvuYmc1YDrTu8DlFLeOuZ7EXN8y6CWLHlnKDIJRNb+y3VLZ3R2JIaJ98S9sdv/b25/OhZhwACCCCAAAIIIIAAAggggAACCCCAAAII7DKBOk3mdpkizUWgXgW0bHq8byRhTS1mNdKaKFGJL1QcRNVrZ2lXxQJyMlfxQdgRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYFsBkrltidgAgd0s4MzEZk7Ll0xcn4g1G9POtVzM7OZe0faABUjmAgblcAgggAACCCCAAAIIIIAAAggggAACCCCAQAEBkrkCMKxG4EUR0B4kB9oa3IkS9zW0D8xvMFThi3J5g+nH+nR3JNI9uRHM0TgKAggggAACCCCAAAIIIIAAAggggAACCCCAQCEBkrlCMqxHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIEgBkrkgNTkWAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAoU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"
    }
   },
   "cell_type": "markdown",
   "id": "1360eccb",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fa77c6",
   "metadata": {},
   "source": [
    "上图为kaggle平台代码提交的代码 理解下这个函数的遍历  以后如果这个训练营说到大模型相关，我们还会经常和os模块打交道"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vs",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
